Energy Minimization Approach to Pixel Labeling Problems in Computer Vision

计算机视觉的目标是自动分析视觉数据，如图像和视频。计算机视觉系统现在用于广泛的应用：医学图像处理，机器人导航，汽车安全，电影特效，图像搜索等，尽管取得了巨大的进步，但人类和计算机视觉系统之间仍然存在很大的差距。 一个被证明对各种视觉问题非常有效的框架是像素标记。它通常是用能量最小化来解决的。这个框架的主要困难是它的计算成本，在实践中产生的许多能量难以最小化。目前被最小化的能量基本上是那些在标签上编码低级别平滑特性的能量，即大多数相邻像素对应该具有相同或相似的标签。然而，在各种视觉任务中，其他属性，例如对像素对之间的标签配置的特定子集的偏好，或对尺寸大于2的像素子集的约束。具有这些更一般的约束的能量仍然难以最小化。我的目标是为这些难以优化的像素标记问题开发有效的能量最小化算法，并利用最小化算法提高实际应用的性能。我的研究主要基于离散优化算法，特别是动态规划和图切割。 建议的应用程序评估的最小化方法，我将开发的前列腺图像中的癌腺体分割体积测量和预后和形状先验的图像分割。更一般地说，我计划开发的算法将为更广泛的应用带来更高的性能，例如对象识别，医学图像分割，运动对应，3D建模等。

The goal of computer vision is to automatically analyze visual data, such as images and video. Computer vision systems are used nowadays for a wide range of applications: medical image processing, robot navigation, automotive safety, special effects in movies, image search, etc. Despite tremendous progress, there is still a large gap between what a human and a computer vision system can do. A framework that has proven very effective for a variety of vision problems is pixel labeling. It is usually addressed with energy minimization. The major difficulty of this framework is its computational cost, with many energies arising in practice being difficult to minimize. The energies that are currently minimized well are essentially those that encode low-level smoothness properties on a labeling, namely, that most adjacent pixel pairs should to have the same or similar labels. However, in a variety of vision tasks, other properties, such as preference for a particular subset of label configurations between pixel pairs, or constraints on pixel subsets of size larger than two arise. Energies with these more general constraints are still difficult to minimize. My goal is to develop efficient energy minimization algorithms for these harder-to-optimize pixel labeling problems, and to improve performance of practical applications utilizing minimization algorithms. My research is heavily based on discrete optimization algorithms, in particular on dynamic programming and graph cuts. The proposed applications for evaluating the minimization methods that I will develop are cancerous gland segmentation in prostate images for volume measuring and prognosis and shape priors for image segmentation. More generally, the algorithms that I plan to develop will lead to an improved performance for a wider range of applications, such as object recognition, medical image segmentation, motion correspondence, 3D modeling, etc.